Overview

Dataset statistics

Number of variables13
Number of observations165
Missing cells3
Missing cells (%)0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory22.1 KiB
Average record size in memory137.3 B

Variable types

Categorical3
Numeric10

Alerts

standard_names has a high cardinality: 165 distinct values High cardinality
ISO_2 has a high cardinality: 165 distinct values High cardinality
ISO_3 has a high cardinality: 165 distinct values High cardinality
Population (2020) is highly correlated with ConfirmedHigh correlation
lat is highly correlated with Infection_rate_f and 1 other fieldsHigh correlation
Confirmed is highly correlated with Population (2020) and 1 other fieldsHigh correlation
Infection_rate_f is highly correlated with lat and 3 other fieldsHigh correlation
Med. Age is highly correlated with lat and 2 other fieldsHigh correlation
Urban Pop % is highly correlated with Infection_rate_f and 1 other fieldsHigh correlation
Population (2020) is highly correlated with ConfirmedHigh correlation
lat is highly correlated with Med. AgeHigh correlation
Confirmed is highly correlated with Population (2020)High correlation
Infection_rate_f is highly correlated with Med. Age and 1 other fieldsHigh correlation
Med. Age is highly correlated with lat and 2 other fieldsHigh correlation
Urban Pop % is highly correlated with Infection_rate_f and 1 other fieldsHigh correlation
Confirmed is highly correlated with Infection_rate_fHigh correlation
Infection_rate_f is highly correlated with ConfirmedHigh correlation
Population (2020) is highly correlated with Confirmed and 1 other fieldsHigh correlation
lat is highly correlated with lng and 2 other fieldsHigh correlation
lng is highly correlated with lat and 1 other fieldsHigh correlation
Confirmed is highly correlated with Population (2020) and 1 other fieldsHigh correlation
Migrants (net) is highly correlated with Population (2020) and 1 other fieldsHigh correlation
Med. Age is highly correlated with lat and 1 other fieldsHigh correlation
Urban Pop % is highly correlated with latHigh correlation
Urban Pop % has 3 (1.8%) missing values Missing
standard_names is uniformly distributed Uniform
ISO_2 is uniformly distributed Uniform
ISO_3 is uniformly distributed Uniform
standard_names has unique values Unique
ISO_2 has unique values Unique
ISO_3 has unique values Unique
Population (2020) has unique values Unique
lat has unique values Unique
lng has unique values Unique
Confirmed has unique values Unique
stringency_index has unique values Unique
Migrants (net) has 2 (1.2%) zeros Zeros

Reproduction

Analysis started2022-05-04 23:49:45.263498
Analysis finished2022-05-04 23:50:04.766343
Duration19.5 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

standard_names
Categorical

HIGH CARDINALITY
UNIFORM
UNIQUE

Distinct165
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size6.6 KiB
Iraq
 
1
Ireland
 
1
Cuba
 
1
Denmark
 
1
South Sudan
 
1
Other values (160)
160 

Length

Max length24
Median length7
Mean length8.054545455
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique165 ?
Unique (%)100.0%

Sample

1st rowAfghanistan
2nd rowAlbania
3rd rowAlgeria
4th rowAngola
5th rowArgentina

Common Values

ValueCountFrequency (%)
Iraq1
 
0.6%
Ireland1
 
0.6%
Cuba1
 
0.6%
Denmark1
 
0.6%
South Sudan1
 
0.6%
Romania1
 
0.6%
Uganda1
 
0.6%
Haiti1
 
0.6%
Latvia1
 
0.6%
Uzbekistan1
 
0.6%
Other values (155)155
93.9%

Length

2022-05-04T19:50:04.905166image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
republic5
 
2.6%
south3
 
1.5%
united3
 
1.5%
guinea2
 
1.0%
new2
 
1.0%
and2
 
1.0%
sudan2
 
1.0%
congo2
 
1.0%
leone1
 
0.5%
timor-leste1
 
0.5%
Other values (173)173
88.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

ISO_2
Categorical

HIGH CARDINALITY
UNIFORM
UNIQUE

Distinct165
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size6.6 KiB
GA
 
1
NP
 
1
UY
 
1
DE
 
1
IT
 
1
Other values (160)
160 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique165 ?
Unique (%)100.0%

Sample

1st rowAF
2nd rowAL
3rd rowDZ
4th rowAO
5th rowAR

Common Values

ValueCountFrequency (%)
GA1
 
0.6%
NP1
 
0.6%
UY1
 
0.6%
DE1
 
0.6%
IT1
 
0.6%
LS1
 
0.6%
SD1
 
0.6%
MZ1
 
0.6%
TW1
 
0.6%
NI1
 
0.6%
Other values (155)155
93.9%

Length

2022-05-04T19:50:05.136759image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ga1
 
0.6%
dk1
 
0.6%
gt1
 
0.6%
ne1
 
0.6%
af1
 
0.6%
ba1
 
0.6%
ci1
 
0.6%
cy1
 
0.6%
ml1
 
0.6%
pa1
 
0.6%
Other values (155)155
93.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

ISO_3
Categorical

HIGH CARDINALITY
UNIFORM
UNIQUE

Distinct165
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size6.6 KiB
SLE
 
1
DZA
 
1
NGA
 
1
MAR
 
1
MUS
 
1
Other values (160)
160 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique165 ?
Unique (%)100.0%

Sample

1st rowAFG
2nd rowALB
3rd rowDZA
4th rowAGO
5th rowARG

Common Values

ValueCountFrequency (%)
SLE1
 
0.6%
DZA1
 
0.6%
NGA1
 
0.6%
MAR1
 
0.6%
MUS1
 
0.6%
GMB1
 
0.6%
BEL1
 
0.6%
KWT1
 
0.6%
TZA1
 
0.6%
NLD1
 
0.6%
Other values (155)155
93.9%

Length

2022-05-04T19:50:05.323541image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
sle1
 
0.6%
brb1
 
0.6%
vnm1
 
0.6%
rwa1
 
0.6%
cod1
 
0.6%
cze1
 
0.6%
fin1
 
0.6%
pse1
 
0.6%
isr1
 
0.6%
usa1
 
0.6%
Other values (155)155
93.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Population (2020)
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct165
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46943332.35
Minimum98453
Maximum1440297825
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.6 KiB
2022-05-04T19:50:05.512255image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum98453
5-th percentile595573.6
Q14669775
median11216250
Q333551824
95-th percentile142589624.8
Maximum1440297825
Range1440199372
Interquartile range (IQR)28882049

Descriptive statistics

Standard deviation159627896.3
Coefficient of variation (CV)3.400438109
Kurtosis64.75845676
Mean46943332.35
Median Absolute Deviation (MAD)9129856
Skewness7.789008121
Sum7745649837
Variance2.548106527 × 1016
MonotonicityNot monotonic
2022-05-04T19:50:05.810867image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
27153401
 
0.6%
50774111
 
0.6%
119484541
 
0.6%
21451941
 
0.6%
89755311
 
0.6%
209972931
 
0.6%
118399181
 
0.6%
96559831
 
0.6%
167584481
 
0.6%
512769771
 
0.6%
Other values (155)155
93.9%
ValueCountFrequency (%)
984531
0.6%
2874371
0.6%
3083371
0.6%
3416281
0.6%
3988451
0.6%
4382021
0.6%
4417501
0.6%
5570261
0.6%
5875411
0.6%
6277041
0.6%
ValueCountFrequency (%)
14402978251
0.6%
13823450851
0.6%
3313410501
0.6%
2740216041
0.6%
2216127851
0.6%
2128219861
0.6%
2069843471
0.6%
1649723481
0.6%
1459455241
0.6%
1291660281
0.6%

lat
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct165
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.53067016
Minimum-40.900557
Maximum64.963051
Zeros0
Zeros (%)0.0%
Negative35
Negative (%)21.2%
Memory size6.6 KiB
2022-05-04T19:50:06.222364image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-40.900557
5-th percentile-23.2196972
Q14.860416
median18.735693
Q339.074208
95-th percentile55.979903
Maximum64.963051
Range105.863608
Interquartile range (IQR)34.213792

Descriptive statistics

Standard deviation24.3412359
Coefficient of variation (CV)1.246308278
Kurtosis-0.5484731924
Mean19.53067016
Median Absolute Deviation (MAD)17.38361
Skewness-0.2950973125
Sum3222.560576
Variance592.4957652
MonotonicityNot monotonic
2022-05-04T19:50:06.536413image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.2104841
 
0.6%
-4.0383331
 
0.6%
-9.1899671
 
0.6%
7.8730541
 
0.6%
-1.8312391
 
0.6%
33.8547211
 
0.6%
-4.6795741
 
0.6%
-8.8742171
 
0.6%
15.7834711
 
0.6%
-0.2280211
 
0.6%
Other values (155)155
93.9%
ValueCountFrequency (%)
-40.9005571
0.6%
-38.4160971
0.6%
-35.6751471
0.6%
-32.5227791
0.6%
-30.5594821
0.6%
-29.6099881
0.6%
-26.5225031
0.6%
-25.2743981
0.6%
-23.4425031
0.6%
-22.3284741
0.6%
ValueCountFrequency (%)
64.9630511
0.6%
61.924111
0.6%
61.524011
0.6%
60.4720241
0.6%
60.1281611
0.6%
58.5952721
0.6%
56.8796351
0.6%
56.263921
0.6%
56.1303661
0.6%
55.3780511
0.6%

lng
Real number (ℝ)

HIGH CORRELATION
UNIQUE

Distinct165
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.16641983
Minimum-106.346771
Maximum178.065032
Zeros0
Zeros (%)0.0%
Negative47
Negative (%)28.5%
Memory size6.6 KiB
2022-05-04T19:50:06.853542image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-106.346771
5-th percentile-84.9164688
Q1-5.54708
median21.824312
Q348.516388
95-th percentile124.9368346
Maximum178.065032
Range284.411803
Interquartile range (IQR)54.063468

Descriptive statistics

Standard deviation61.36728173
Coefficient of variation (CV)2.899275466
Kurtosis-0.02050563335
Mean21.16641983
Median Absolute Deviation (MAD)27.371392
Skewness0.05836967483
Sum3492.459273
Variance3765.943267
MonotonicityNot monotonic
2022-05-04T19:50:07.115923image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
28.2336081
 
0.6%
84.1240081
 
0.6%
11.6094441
 
0.6%
22.9375061
 
0.6%
78.962881
 
0.6%
-19.0208351
 
0.6%
-66.589731
 
0.6%
-7.092621
 
0.6%
17.6790761
 
0.6%
34.3015251
 
0.6%
Other values (155)155
93.9%
ValueCountFrequency (%)
-106.3467711
0.6%
-102.5527841
0.6%
-95.98823681
0.6%
-95.7128911
0.6%
-90.2307591
0.6%
-88.896531
0.6%
-88.497651
0.6%
-86.2419051
0.6%
-85.2072291
0.6%
-83.7534281
0.6%
ValueCountFrequency (%)
178.0650321
0.6%
174.8859711
0.6%
166.9591581
0.6%
160.1561941
0.6%
143.955551
0.6%
138.2529241
0.6%
133.7751361
0.6%
127.7669221
0.6%
125.7275391
0.6%
121.7740171
0.6%

Density (P/Km²)
Real number (ℝ≥0)

Distinct115
Distinct (%)69.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean205.6
Minimum2
Maximum8358
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.6 KiB
2022-05-04T19:50:07.361272image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile5.4
Q130
median83
Q3152
95-th percentile606.2
Maximum8358
Range8356
Interquartile range (IQR)122

Descriptive statistics

Standard deviation688.8905078
Coefficient of variation (CV)3.350634765
Kurtosis121.5734071
Mean205.6
Median Absolute Deviation (MAD)57
Skewness10.44758777
Sum33924
Variance474570.1317
MonotonicityNot monotonic
2022-05-04T19:50:07.710203image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
257
 
4.2%
45
 
3.0%
185
 
3.0%
834
 
2.4%
263
 
1.8%
163
 
1.8%
203
 
1.8%
1373
 
1.8%
173
 
1.8%
762
 
1.2%
Other values (105)127
77.0%
ValueCountFrequency (%)
21
 
0.6%
32
 
1.2%
45
3.0%
51
 
0.6%
71
 
0.6%
81
 
0.6%
92
 
1.2%
111
 
0.6%
131
 
0.6%
151
 
0.6%
ValueCountFrequency (%)
83581
0.6%
22391
0.6%
13801
0.6%
12651
0.6%
8471
0.6%
6731
0.6%
6681
0.6%
6671
0.6%
6261
0.6%
5271
0.6%

Confirmed
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct165
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean514937.5212
Minimum1
Maximum17459296
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.6 KiB
2022-05-04T19:50:07.942564image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile378.2
Q16955
median65697
Q3252724
95-th percentile2124367.6
Maximum17459296
Range17459295
Interquartile range (IQR)245769

Descriptive statistics

Standard deviation1781181.315
Coefficient of variation (CV)3.459024137
Kurtosis55.28120062
Mean514937.5212
Median Absolute Deviation (MAD)63211
Skewness6.816804724
Sum84964691
Variance3.172606877 × 1012
MonotonicityNot monotonic
2022-05-04T19:50:08.193621image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9431
 
0.6%
462741
 
0.6%
98931
 
0.6%
943711
 
0.6%
1491491
 
0.6%
937721
 
0.6%
2021
 
0.6%
64711
 
0.6%
36961
 
0.6%
175591
 
0.6%
Other values (155)155
93.9%
ValueCountFrequency (%)
11
0.6%
171
0.6%
311
0.6%
411
0.6%
461
0.6%
1521
0.6%
2021
0.6%
3071
0.6%
3621
0.6%
4431
0.6%
ValueCountFrequency (%)
174592961
0.6%
94628091
0.6%
71629781
0.6%
66652091
0.6%
53524491
0.6%
27648431
0.6%
22460321
0.6%
21738961
0.6%
21599371
0.6%
19820901
0.6%

stringency_index
Real number (ℝ≥0)

UNIQUE

Distinct165
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.34675149
Minimum11.83505376
Maximum75.0536828
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.6 KiB
2022-05-04T19:50:08.484609image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum11.83505376
5-th percentile31.15885032
Q143.16027778
median51.73650627
Q358.62871657
95-th percentile66.26470353
Maximum75.0536828
Range63.21862903
Interquartile range (IQR)15.46843879

Descriptive statistics

Standard deviation11.81172767
Coefficient of variation (CV)0.234607543
Kurtosis0.7045174984
Mean50.34675149
Median Absolute Deviation (MAD)7.547217989
Skewness-0.7094207914
Sum8307.213995
Variance139.5169104
MonotonicityNot monotonic
2022-05-04T19:50:08.930310image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
58.997873051
 
0.6%
39.591541221
 
0.6%
24.497489531
 
0.6%
47.300186381
 
0.6%
39.251446241
 
0.6%
45.511814051
 
0.6%
57.267984181
 
0.6%
44.212125391
 
0.6%
71.199127241
 
0.6%
41.89244811
 
0.6%
Other values (155)155
93.9%
ValueCountFrequency (%)
11.835053761
0.6%
12.43396231
0.6%
13.849461471
0.6%
21.555584231
0.6%
23.502411291
0.6%
24.497489531
0.6%
28.683466851
0.6%
29.630524931
0.6%
31.102298171
0.6%
31.385058921
0.6%
ValueCountFrequency (%)
75.05368281
0.6%
73.698875451
0.6%
71.199127241
0.6%
70.094249911
0.6%
68.3918411
0.6%
67.828682671
0.6%
67.302524191
0.6%
66.874263441
0.6%
66.360124491
0.6%
65.883019711
0.6%

Infection_rate_f
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct164
Distinct (%)99.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.393537576
Minimum0.0003
Maximum8.2295
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.6 KiB
2022-05-04T19:50:09.292923image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0.0003
5-th percentile0.0062
Q10.0895
median0.6531
Q32.3356
95-th percentile4.88476
Maximum8.2295
Range8.2292
Interquartile range (IQR)2.2461

Descriptive statistics

Standard deviation1.704021819
Coefficient of variation (CV)1.222802922
Kurtosis2.190307891
Mean1.393537576
Median Absolute Deviation (MAD)0.6302
Skewness1.498731027
Sum229.9337
Variance2.903690361
MonotonicityNot monotonic
2022-05-04T19:50:10.015598image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.00622
 
1.2%
0.46281
 
0.6%
0.05511
 
0.6%
2.2721
 
0.6%
4.90511
 
0.6%
0.22551
 
0.6%
0.15321
 
0.6%
1.51691
 
0.6%
0.02291
 
0.6%
0.1031
 
0.6%
Other values (154)154
93.3%
ValueCountFrequency (%)
0.00031
0.6%
0.00041
0.6%
0.00081
0.6%
0.00161
0.6%
0.00211
0.6%
0.00241
0.6%
0.00251
0.6%
0.0051
0.6%
0.00622
1.2%
0.00681
0.6%
ValueCountFrequency (%)
8.22951
0.6%
8.19661
0.6%
5.69441
0.6%
5.35441
0.6%
5.26921
0.6%
5.25311
0.6%
5.11561
0.6%
4.98111
0.6%
4.90511
0.6%
4.80341
0.6%

Migrants (net)
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct137
Distinct (%)83.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean393.9515152
Minimum-653249
Maximum954806
Zeros2
Zeros (%)1.2%
Negative91
Negative (%)55.2%
Memory size6.6 KiB
2022-05-04T19:50:10.381705image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-653249
5-th percentile-113277.4
Q1-16053
median-1500
Q314000
95-th percentile156385.4
Maximum954806
Range1608055
Interquartile range (IQR)30053

Descriptive statistics

Standard deviation135905.2294
Coefficient of variation (CV)344.9795829
Kurtosis20.54229241
Mean393.9515152
Median Absolute Deviation (MAD)15056
Skewness1.204632383
Sum65002
Variance1.847023137 × 1010
MonotonicityNot monotonic
2022-05-04T19:50:10.628742image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-40004
 
2.4%
-100004
 
2.4%
400004
 
2.4%
-300003
 
1.8%
-400003
 
1.8%
02
 
1.2%
20002
 
1.2%
12002
 
1.2%
-200002
 
1.2%
40002
 
1.2%
Other values (127)137
83.0%
ValueCountFrequency (%)
-6532491
0.6%
-5326871
0.6%
-4273911
0.6%
-3695011
0.6%
-3483991
0.6%
-2333791
0.6%
-1742001
0.6%
-1633131
0.6%
-1168581
0.6%
-989551
0.6%
ValueCountFrequency (%)
9548061
0.6%
5438221
0.6%
2839221
0.6%
2606501
0.6%
2420321
0.6%
2047961
0.6%
1824561
0.6%
1686941
0.6%
1582461
0.6%
1489431
0.6%

Med. Age
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct33
Distinct (%)20.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.26060606
Minimum15
Maximum48
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.6 KiB
2022-05-04T19:50:11.001202image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum15
5-th percentile18
Q121
median30
Q338
95-th percentile44.8
Maximum48
Range33
Interquartile range (IQR)17

Descriptive statistics

Standard deviation9.234675486
Coefficient of variation (CV)0.3051715312
Kurtosis-1.255095604
Mean30.26060606
Median Absolute Deviation (MAD)9
Skewness0.1527542968
Sum4993
Variance85.27923134
MonotonicityNot monotonic
2022-05-04T19:50:11.338897image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
1912
 
7.3%
2811
 
6.7%
1810
 
6.1%
439
 
5.5%
429
 
5.5%
329
 
5.5%
307
 
4.2%
207
 
4.2%
407
 
4.2%
387
 
4.2%
Other values (23)77
46.7%
ValueCountFrequency (%)
151
 
0.6%
161
 
0.6%
176
3.6%
1810
6.1%
1912
7.3%
207
4.2%
215
3.0%
223
 
1.8%
233
 
1.8%
246
3.6%
ValueCountFrequency (%)
481
 
0.6%
471
 
0.6%
463
 
1.8%
454
2.4%
443
 
1.8%
439
5.5%
429
5.5%
414
2.4%
407
4.2%
387
4.2%

Urban Pop %
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct71
Distinct (%)43.8%
Missing3
Missing (%)1.8%
Infinite0
Infinite (%)0.0%
Mean59.65432099
Minimum13
Maximum98
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.6 KiB
2022-05-04T19:50:11.753585image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum13
5-th percentile23
Q143
median60
Q378.75
95-th percentile91.95
Maximum98
Range85
Interquartile range (IQR)35.75

Descriptive statistics

Standard deviation21.83288334
Coefficient of variation (CV)0.3659899732
Kurtosis-0.9333171539
Mean59.65432099
Median Absolute Deviation (MAD)18
Skewness-0.2636859858
Sum9664
Variance476.6747949
MonotonicityNot monotonic
2022-05-04T19:50:12.082468image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
576
 
3.6%
805
 
3.0%
435
 
3.0%
565
 
3.0%
734
 
2.4%
784
 
2.4%
384
 
2.4%
634
 
2.4%
764
 
2.4%
524
 
2.4%
Other values (61)117
70.9%
ValueCountFrequency (%)
131
 
0.6%
141
 
0.6%
171
 
0.6%
183
1.8%
212
1.2%
232
1.2%
242
1.2%
252
1.2%
261
 
0.6%
272
1.2%
ValueCountFrequency (%)
981
 
0.6%
962
1.2%
941
 
0.6%
933
1.8%
922
1.2%
911
 
0.6%
891
 
0.6%
884
2.4%
873
1.8%
863
1.8%

Interactions

2022-05-04T19:50:02.583717image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-04T19:49:48.158503image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-04T19:49:50.014179image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-04T19:49:51.373250image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-04T19:49:52.791319image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-04T19:49:54.144095image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-04T19:49:55.647655image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-04T19:49:57.093886image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-04T19:49:58.483364image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2022-05-04T19:50:02.724628image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2022-05-04T19:50:03.797235image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-04T19:49:49.871390image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2022-05-04T19:49:52.670478image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-04T19:49:54.024644image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-04T19:49:55.505478image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-04T19:49:56.956161image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-04T19:49:58.340948image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-04T19:50:00.925063image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-04T19:50:02.446134image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Correlations

2022-05-04T19:50:12.350801image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-05-04T19:50:12.653573image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-05-04T19:50:12.959554image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-05-04T19:50:13.261004image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-05-04T19:50:04.039042image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
A simple visualization of nullity by column.
2022-05-04T19:50:04.426936image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-05-04T19:50:04.617866image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

standard_namesISO_2ISO_3Population (2020)latlngDensity (P/Km²)Confirmedstringency_indexInfection_rate_fMigrants (net)Med. AgeUrban Pop %
0AfghanistanAFAFG3907428033.93911067.7099536046274.042.7005140.1183-62920.01825.0
1AlbaniaALALB287723941.15333220.16833110552004.055.3300561.8074-14000.03663.0
2AlgeriaDZDZA4398456928.0338861.6596261894371.059.9814440.2146-10000.02973.0
3AngolaAOAGO33032075-11.20269217.8738872616562.057.5411040.05026413.01767.0
4ArgentinaARARG45267449-38.416097-63.616672171531374.071.1991273.38294800.03293.0
5AustraliaAUAUS25550683-25.274398133.775136327912.053.7242140.1091158246.03886.0
6AustriaATAUT901536147.51623114.550072109282456.043.8188263.133165000.04357.0
7AzerbaijanAZAZE1015497840.14310547.576927123195422.064.8176431.92431200.03256.0
8BahrainBHBHR171105726.06670050.557700223989883.056.2584615.253147800.03289.0
9BangladeshBDBGD16497234823.68499490.3563311265498293.065.8830200.3020-369501.02839.0

Last rows

standard_namesISO_2ISO_3Population (2020)latlngDensity (P/Km²)Confirmedstringency_indexInfection_rate_fMigrants (net)Med. AgeUrban Pop %
155United KingdomGBGBR6794828255.378051-3.4359732812173896.056.8885533.1993260650.04083.0
156United StatesUSUSA33134105037.090240-95.7128913617459296.056.0458805.2692954806.03883.0
157UruguayUYURY3475842-32.522779-55.7658352012451.037.6648050.3582-3000.03696.0
158UzbekistanUZUZB3355182441.37749164.5852627975675.050.9683750.2255-8863.02850.0
159VanuatuVUVUT308337-15.376706166.959158251.038.5896240.0003120.02124.0
160VenezuelaVEVEN284215816.423750-66.58973032109395.067.8286830.3848-653249.030NaN
161VietnamVNVNM9749001314.058324108.2771993141410.056.7859640.0016-80000.03238.0
162YemenYEYEM2993546815.55272748.516388564023.029.6305250.0135-30000.02038.0
163ZambiaZMZMB18468257-13.13389727.8493322518575.038.7582790.1006-8000.01845.0
164ZimbabweZWZWE14899771-19.01543829.1548573812087.061.0085500.0812-116858.01938.0